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Spatial Algorithms Syst."],"published-print":{"date-parts":[[2020,9,30]]},"abstract":"<jats:p>\n            Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data are updated weekly and provided at a spatially coarse resolution. Producing timely and reliable high-resolution spatiotemporal forecasts for ILI is crucial for local preparedness and optimal interventions. We present\n            <jats:italic toggle=\"yes\">T<\/jats:italic>\n            heory-guided\n            <jats:italic toggle=\"yes\">D<\/jats:italic>\n            eep Learning-based\n            <jats:italic toggle=\"yes\">E<\/jats:italic>\n            pidemic\n            <jats:italic toggle=\"yes\">F<\/jats:italic>\n            orecasting with\n            <jats:italic toggle=\"yes\">S<\/jats:italic>\n            ynthetic\n            <jats:italic toggle=\"yes\">I<\/jats:italic>\n            nformation (TDEFSI),\n            <jats:sup>1<\/jats:sup>\n            an epidemic forecasting framework that integrates the strengths of deep neural networks and high-resolution simulations of epidemic processes over networks. TDEFSI yields accurate high-resolution spatiotemporal forecasts using low-resolution time-series data.\n          <\/jats:p>\n          <jats:p>During the training phase, TDEFSI uses high-resolution simulations of epidemics that explicitly model spatial and social heterogeneity inherent in urban regions as one component of training data. We train a two-branch recurrent neural network model to take both within-season and between-season low-resolution observations as features and output high-resolution detailed forecasts. The resulting forecasts are not just driven by observed data but also capture the intricate social, demographic, and geographic attributes of specific urban regions and mathematical theories of disease propagation over networks.<\/jats:p>\n          <jats:p>We focus on forecasting the incidence of ILI and evaluate TDEFSI\u2019s performance using synthetic and real-world testing datasets at the state and county levels in the USA. The results show that, at the state level, our method achieves comparable\/better performance than several state-of-the-art methods. At the county level, TDEFSI outperforms the other methods. The proposed method can be applied to other infectious diseases as well.<\/jats:p>","DOI":"10.1145\/3380971","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T13:36:07Z","timestamp":1588599367000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["TDEFSI"],"prefix":"10.1145","volume":"6","author":[{"given":"Lijing","family":"Wang","sequence":"first","affiliation":[{"name":"Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative 8 Department of Computer Science, University of Virginia, Charlottesville, VA"}]},{"given":"Jiangzhuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA"}]},{"given":"Madhav","family":"Marathe","sequence":"additional","affiliation":[{"name":"Network Systems Science and Advanced Computing Division, Biocomplexity Institute and Initiative 8 Department of Computer Science, University of Virginia, Charlottesville, VA"}]}],"member":"320","published-online":{"date-parts":[[2020,4,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"ACS. 2009-2013. 2009-2013 5-Year American Community Survey Commuting Flows. 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Charles Griffin 8 Company Ltd 5a, High Wycombe, Bucks, UK."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBE.2015.7367640"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/1995456.1995598"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623375"},{"key":"e_1_2_1_8_1","first-page":"415","article-title":"Creating synthetic baseline populations","volume":"30","author":"Beckman Richard J.","year":"1996","unstructured":"Richard J. Beckman, Keith A. Baggerly, and Michael D. McKay. 1996. Creating synthetic baseline populations. Transport. Res. A 30, 6 (1996), 415--429.","journal-title":"Transport. Res. 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Retrived August 28, 2018 from https:\/\/www.ncdc.noaa.gov\/cdo-web\/datasets."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000656"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0094130"},{"key":"e_1_2_1_24_1","volume-title":"Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995","author":"Cui Zhicheng","year":"2016","unstructured":"Zhicheng Cui, Wenlin Chen, and Yixin Chen. 2016. Multi-scale convolutional neural networks for time series classification. arXiv preprint arXiv:1603.06995 (2016)."},{"key":"e_1_2_1_25_1","volume-title":"Graph message passing with cross-location attentions for long-term ILI prediction. arXiv preprint arXiv:1912.10202","author":"Deng Songgaojun","year":"2019","unstructured":"Songgaojun Deng, Shusen Wang, Huzefa Rangwala, Lijing Wang, and Yue Ning. 2019. 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In ECML\/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2016, Riva Del Garda, Italy."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtbi.2011.10.008"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.aje.a114253"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/0277-9536(95)00205-7"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mbs.2009.03.008"},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0022461"},{"key":"e_1_2_1_60_1","volume-title":"Megapixel size image creation using generative adversarial networks. arXiv preprint arXiv:1706.00082","author":"Marchesi Marco","year":"2017","unstructured":"Marco Marchesi. 2017. Megapixel size image creation using generative adversarial networks. arXiv preprint arXiv:1706.00082 (2017)."},{"key":"e_1_2_1_61_1","first-page":"683","article-title":"Collaborative efforts to forecast seasonal influenza in the United States, 2015--2016. Sci","volume":"9","author":"McGowan Craig J.","year":"2019","unstructured":"Craig J. McGowan, Matthew Biggerstaff, Michael Johansson, Karyn M. Apfeldorf, Michal Ben-Nun, Logan Brooks, Matteo Convertino, Madhav Erraguntla, David C. Farrow, John Freeze, et al. 2019. Collaborative efforts to forecast seasonal influenza in the United States, 2015--2016. Sci. Rep. 9, 1 (2019), 683.","journal-title":"Rep."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.vaccine.2007.03.046"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1111\/irv.12594"},{"key":"e_1_2_1_64_1","unstructured":"NDSSL. 2014. Synthetic Data of Montgomery County Virginia. 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The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621 (2017)."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1812594116"},{"key":"e_1_2_1_74_1","volume-title":"Effectiveness of data augmentation in cellular-based localization using deep learning. arXiv preprint arXiv:1906.08171","author":"Rizk Hamada","year":"2019","unstructured":"Hamada Rizk, Ahmed Shokry, and Moustafa Youssef. 2019. Effectiveness of data augmentation in cellular-based localization using deep learning. arXiv preprint arXiv:1906.08171 (2019)."},{"key":"e_1_2_1_75_1","volume-title":"Proceedings of the Annual Conference of the International Society for Music Information Retrieval (ISMIR\u201915)","author":"Schl\u00fcter Jan","year":"2015","unstructured":"Jan Schl\u00fcter and Thomas Grill. 2015. Exploring data augmentation for improved singing voice detection with neural networks. In Proceedings of the Annual Conference of the International Society for Music Information Retrieval (ISMIR\u201915). 121--126."},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1208772109"},{"key":"e_1_2_1_77_1","volume-title":"Real-time influenza forecasts during the 2012-2013 season. Nature Commun. 4, 2837","author":"Shaman Jeffrey","year":"2013","unstructured":"Jeffrey Shaman, Alicia Karspeck, Wan Yang, James Tamerius, and Marc Lipsitch. 2013. Real-time influenza forecasts during the 2012-2013 season. Nature Commun. 4, 2837 (2013)."},{"key":"e_1_2_1_78_1","unstructured":"Thinglink. 2019. New Jersey Regions Map. Retrieved from https:\/\/www.thinglink.com\/scene\/788830737167024130."},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1503\/cmaj.091807"},{"key":"e_1_2_1_80_1","volume-title":"Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kuli\u0107.","author":"Um Terry Taewoong","year":"2017","unstructured":"Terry Taewoong Um, Franz Michael Josef Pfister, Daniel Pichler, Satoshi Endo, Muriel Lang, Sandra Hirche, Urban Fietzek, and Dana Kuli\u0107. 2017. Data augmentation of wearable sensor data for parkinson\u2019s disease monitoring using convolutional neural networks. arXiv preprint arXiv:1706.00527 (2017)."},{"key":"e_1_2_1_81_1","volume-title":"Increasing deep learning melanoma classification by classical and expert knowledge based image transforms. CoRR, abs\/1702.07025 1","author":"Vasconcelos Cristina Nader","year":"2017","unstructured":"Cristina Nader Vasconcelos and B\u00e1rbara Nader Vasconcelos. 2017. Increasing deep learning melanoma classification by classical and expert knowledge based image transforms. CoRR, abs\/1702.07025 1 (2017)."},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2888585"},{"key":"e_1_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwg239"},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.epidem.2017.08.002"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0188941"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-018-0608-8"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019607"},{"key":"e_1_2_1_88_1","volume-title":"TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information (Supplement).","author":"Wang Lijing","year":"2019","unstructured":"Lijing Wang, Jiangzhuo Chen, and Madhav Marathe. 2019. 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